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Awesome Digital and Computational Pathology Awesome

This curated list of useful resources is supported by:

Paige AI

Contents

Software

Assistant

Image Analysis

  • HistomicsTK - Toolkit for the analysis of digital pathology images.
  • HistoQC - Quality control tools for digital pathology.
  • PathProfiler - Quality assessment of histopathology WSI cohorts.
  • PyHIST - Histological image segmentation tool.
  • pyslide - Digital pathology WSI analysis toolbox.
  • TIA Toolbox - Computational pathology toolbox that provides an end-to-end API for pathology image analysis.

Image IO

  • Bio-Formats - Java software tool for reading and writing microscopy image using standardized, open formats.
  • compay-syntax - Tissue mask and tiling pipeline.
  • cuCIM - NVIDIA's accelerated computer vision and image processing software library for multidimensional images.
  • libvips - A fast image processing library with low memory needs.
  • OpenSlide - Provides a simple C interface with Python bindings to read WSIs in multiple formats.
  • svg2svs - Generate checkerboard and build multi-layer pyramidal SVS files from SVG images.
  • tifffile - Read and write TIFF-like files using in bioimaging.
  • WholeSlideData - Batch iterator that enables fast, efficient and easy patch sampling.

Machine Learning

  • DLUP - Deep learning utilities for pathology.
  • eva - Evaluation framework for oncology foundation models.
  • histocartography - Library designed to facilitate the development of graph-based computational pathology pipelines.
  • nuclei.io - Human-in-the-loop active learning framework for pathology image analysis.
  • PathML - Tools for computational pathology.
  • Slideflow - Python package that provides a unified API for building and testing deep learning models for histopathology.

Model

  • ACMIL - WSI classification.
  • BEPH - BEiT-based model pre-training on WSIs.
  • Cell-DETR - Attention-based transformers for instance segmentation of cells in microstructures.
  • CellViT - Vision transformers for precise cell segmentation and classification.
  • Cerberus - Multi-task learning enables simultaneous histology image segmentation and classification.
  • CLAM - Data-efficient and weakly supervised computational pathology on WSI.
  • DeepLIIF - Deep-learning inferred multiplex immunofluorescence for immunohistochemical image quantification.
  • DiffInfinite - Large mask-image synthesis via parallel random patch diffusion in histopathology.
  • DMMN - Deep Multi-Magnification Network for multi-class tissue segmentation of WSI.
  • DT-MIL - Deformable transformer for multi-instance learning on histopathological image.
  • FrOoDo - Framework for out of distribution detection.
  • HistoGPT - Generating highly accurate histopathology reports from whole slide images.
  • HistoSegNet - Semantic segmentation of histological tissue type in WSIs.
  • HoVer-Net - Simultaneous segmentation and classification of nuclei in multi-tissue histology images.
  • LongViT - Vision Transformer that can process gigapixel images in an end-to-end manner.
  • MCAT - Multimodal co-attention transformer for survival prediction in gigapixel WSIs.
  • MMP - Multimodal prototyping for cancer survival prediction.
  • MSINet - Deep learning model for the prediction of microsatellite instability in colorectal cancer.
  • PANTHER - Morphological prototyping for unsupervised slide representation learning in computational pathology.
  • Patch-GCN - WSI are 2D point clouds: Context-aware survival prediction using patch-based graph convolutional networks.
  • RSP - Self-supervised driven consistency training for annotation efficient histopathology image analysis.
  • SparseConvMIL - Sparse convolutional context-aware multiple instance learning for WSI classification.
  • StainGAN - Stain style transfer for digital histological images.
  • stainlib - Augmentation & normalization of H&E images.
  • StainTools - Tools for tissue image stain normalisation and augmentation.
  • StarDist - Object detection with star-convex shapes.
  • TANGLE - Transcriptomics-guided slide representation learning in computational pathology.
  • TCGA segmentation - Weakly supervised multiple instance learning histopathological tumor segmentation.
  • torchstain - Stain normalization transformations.
  • TransMIL - Transformer based correlated multiple instance learning for WSI classification.

Foundation Model

  • CONCH - Vision-language foundation model for computational pathology.
  • Hibou - A family of foundational vision transformers for pathology.
  • HIPT - Scaling vision transformers to gigapixel images via hierarchical self-supervised learning.
  • H-optimus - Foundation model for histology.
  • PathDino - Rotation-agnostic image representation learning for digital pathology.
  • Phikon - Scaling self-supervised learning for histopathology with masked image modeling.
  • Prov-GigaPath - A whole-slide foundation model for digital pathology from real-world data.
  • ROAM - A transformer-based weakly supervised computational pathology method for clinical-grade diagnosis and molecular state revelation of gliomas.
  • TransPath - Transformer-based unsupervised contrastive learning for histopathological image classification.
  • UNI - General-purpose foundation model for computational pathology.
  • VIM4Path - Self-supervised vision mamba for WSIs.
  • Virchow - Self-supervised vision transformer pretrained using 1.5M WSIs.

Platform

  • Digital Slide Archive - Provides the ability to store, manage, visualize and annotate large imaging datasets.

Viewer

  • ASAP - Desktop application for visualizing, annotating and automatically analyzing WSIs.
  • Cytomine - Collaborative analysis of WSIs.
  • DigiPathAI - Tool to visualize gigantic pathology images and use AI to segment cancer cells and present as an overlay.
  • HistomicsUI - Web interface to visualize WSI and manage annotations.
  • slim - Interoperable web-based slide microscopy viewer and annotation tool.
  • QuickAnnotator - Model assisted tool for rapid annotation of WSIs.
  • QuPath - Java application that enables researchers and pathologists to visualize, analyze and annotate WSIs.

Viewer (Free)

  • Aperio ImageScope - Freely downloadable software for viewing WSIs. Windows only.
  • PathPresenter - A complete enterprise workflow platform built by pathologists.

Data

Challenges

  • ACDC - Automatic Cancer Detection and Classification of lung histopathology.
  • ACROBAT - AutomatiC Registration Of Breast cAncer Tissue.
  • ANHIR - Automatic Non-rigid Histological Image Registration.
  • BACH - BreAst Cancer Histology images.
  • BCI - Breast Cancer Immunohistochemical image generation.
  • BreastPathQ - Quantitative biomarkers for the determination of cancer cellularity.
  • CAMELYON16 - Cancer metastasis detection in lymph node.
  • CAMELYON17 - Building on CAMELYON16 by moving from slide level analysis to patient level analysis.
  • CellSeg - Cell segmentation in multi-modality high-resolution microscopy images.
  • CoNIC - Colon Nuclei Identification and Counting.
  • DigestPath 2019 - Digestive-system pathological detection and segmentation.
  • ENDO-AID - Endometrial carcinoma detection in pipelle biopsies.
  • Gleason 2019 - Automatic Gleason grading of prostate cancer in digital histopathology.
  • HER2 Scoring Contest - Automated HER2 scoring algorithms in WSI of breast cancer tissues.
  • HEROHE - Predicting HER2 status in breast cancer from H&E.
  • KPIs - Kidney Pathology Image segmentation.
  • LEOPARD - LEarning biOchemical Prostate cAncer Recurrence from histopathology sliDes.
  • LYSTO - LYmphocytes aSsessment hackathOn in immunohistochemically stained tissue sections.
  • LYON19 - LYmphocyte detectiON in IHC stained specimens.
  • MIDOG 2021 - MItosis DOmain Generalization on tissue preparation and image acquisition.
  • MIDOG 2022 - MItosis DOmain Generalization on tissue types.
  • MITOS-ATYPIA-14 - Detection of mitosis and evaluation of nuclear atypia score.
  • MoNuSAC - Multi-Organ NUclei Segmentation And Classification.
  • MoNuSeg - Multi-Organ NUclei Segmentation.
  • PAIP2019 - Liver cancer segmentation.
  • PAIP2020 - Classification and segmentation of microsatellite instability (MSI) in colorectal cancer.
  • PAIP2021 - Perineural invasion in multiple organ cancer.
  • PAIP2023 - Tumor cellularity prediction in pancreatic cancer and colon cancer.
  • PANDA - Prostate cANcer graDe Assessment.
  • SegPC - Segmentation of multiple myeloma in Plasma Cells.
  • TIGER - Fully automated assessment of tumor-infiltrating lymphocytes (TILs) in H&E breast cancer slides.
  • TUPAC16 - TUmor Proliferation Assessment.
  • WSSS4LUAD - Weakly-supervised tissue semantic segmentation for lung adenocarcinoma.

Datasets

  • ARCH - Multiple instance captioning.
  • BCNB - Early Breast Cancer Core-Needle Biopsy WSI dataset.
  • BCSS - Breast Cancer Semantic Segmentation.
  • BRACS - BReAst Carcinoma Subtyping.
  • CRC - 100,000 histological images of human colorectal cancer and healthy tissue.
  • CryoNuSeg - Nuclei segmentation of cryosectioned H&E-stained histological images.
  • H2T - Handcrafted Histological Transformer for unsupervised representation of WSIs.
  • HEST - Bringing spatial transcriptomics and histopathology together.
  • LC25000 - Lung and colon cancer histopathological image dataset.
  • LyNSeC - Lymphoma nuclear segmentation and classification dataset.
  • MHIST - Minimalist histopathology image analysis dataset.
  • NuInsSeg - A fully annotated dataset for nuclei instance segmentation in H&E-stained histological images.
  • NuCLS - A scalable crowdsourcing approach & dataset for nucleus classification, localization and segmentation in breast cancer.
  • OCELOT - Overlapped cell on tissue dataset for histopathology.
  • PanNuke - Dataset for nuclei instance segmentation and classification.
  • PCAM - PatchCamelyon provides a new benchmark for machine learning models akin to CIFAR-10 and MNIST.
  • TCGA - The Cancer Genome Atlas is a landmark cancer genomics program that molecularly characterized over 20,000 primary cancer and matched normal samples spanning 33 cancer types.
  • UNITOPATHO - A labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading.
  • UNMaSk - Unmasking the immune microecology of ductal carcinoma in situ.

References

Publications

Papers

  • chen2022self - Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology.
  • kang2022benchmarking - Benchmarking Self-Supervised Learning on Diverse Pathology Datasets.
  • wolflein2023good - A Good Feature Extractor Is All You Need for Weakly Supervised Pathology Slide Classification.
  • vaidya2024demographic - Demographic bias in misdiagnosis by computational pathology models.